Detecting hand’s tremor using leap motion controller in guiding surgical robot arms and laparoscopic scissors

Abstract

UtilizingNatural User Interfaces (NUIs) to access the patient's data in sterile environments is a practical way tomaintain sterility in the operating rooms orto perform remote surgery.Leap Motion Controller (LMC) has recently gained popularity as a NUI with high measurement accuracy, portability, low cost, and excellent data processing capabilities. This article discusses the possibility of using LMC to directly control surgical robot arms and laparoscopic scissors during surgical procedures. Fatigue-Induced Tremor (FIT) is an unavoidable phenomenon that significantly impairs accuracy in long duration laparoscopic surgeries. Determining this tremor when its properties change with increasing muscle fatigue is one of the most effective methods to improve surgical accuracy. Tremors can be divided into resting and active types. This study aims to evaluate the effects of FIT measured by LMC on the amount of error in controlling the surgical robot arm and laparoscopic scissors. Therefore, it is recommended that some specific movement patterns be developed to measure FIT when the surgeon's hand is active. As the LMC's data is in the time domain, it has been converted to a frequency domain using a Fast Fourier Transform (FFT). Intending to evaluate the surgical robot’s performance with and without hand tremors, the hand motion are considered as a manipulator of surgical robot arm, then the tracking errors in both cases are compared. The effect of tremor on tracking the path and the importance of eliminating the error will be discussed. For this purpose, two types of surgical robot arms and laparoscopic scissors have been used. The primary surgical robot have six degrees of freedom (DoF) and is equipped with a mechanism similar to the grasping tool used in laparoscopic surgeries. A second one consists of an arm with three DoF, that can guide and operate a two DoF laparoscopic scissors. The results indicate that FIT in the hand of a healthy surgeon may lead to an increase in the robot’s tracking error of up to 2.5% in long-duration non-invasive surgeries.